4 research outputs found
Voice pathologies : the most comum features and classification tools
Speech pathologies are quite common in society, however the exams that exist are invasive, making them uncomfortable for patients and depending on the experience of the clinician who performs the assessment. Hence the need to develop non-invasive methods, which allow objective and efficient analysis. Taking this need into account in this work, the most promising list of features and classifiers was identified. As features, jitter, shimmer, HNR, LPC, PLP, and MFCC were identified and as classifiers CNN, RNN and LSTM. This study intends to develop a device to support medical decision, however this article already presents the system interface.info:eu-repo/semantics/publishedVersio
A Voice Disease Detection Method Based on MFCCs and Shallow CNN
The incidence rate of voice diseases is increasing year by year. The use of
software for remote diagnosis is a technical development trend and has
important practical value. Among voice diseases, common diseases that cause
hoarseness include spasmodic dysphonia, vocal cord paralysis, vocal nodule, and
vocal cord polyp. This paper presents a voice disease detection method that can
be applied in a wide range of clinical. We cooperated with Xiangya Hospital of
Central South University to collect voice samples from sixty-one different
patients. The Mel Frequency Cepstrum Coefficient (MFCC) parameters are
extracted as input features to describe the voice in the form of data. An
innovative model combining MFCC parameters and single convolution layer CNN is
proposed for fast calculation and classification. The highest accuracy we
achieved was 92%, it is fully ahead of the original research results and
internationally advanced. And we use Advanced Voice Function Assessment
Databases (AVFAD) to evaluate the generalization ability of the method we
proposed, which achieved an accuracy rate of 98%. Experiments on clinical and
standard datasets show that for the pathological detection of voice diseases,
our method has greatly improved in accuracy and computational efficiency